Overview

Dataset statistics

Number of variables22
Number of observations2126
Missing cells0
Missing cells (%)0.0%
Duplicate rows11
Duplicate rows (%)0.5%
Total size in memory365.5 KiB
Average record size in memory176.1 B

Variable types

Numeric19
Categorical3

Alerts

Dataset has 11 (0.5%) duplicate rowsDuplicates
baseline value is highly overall correlated with histogram_mode and 2 other fieldsHigh correlation
light_decelerations is highly overall correlated with mean_value_of_short_term_variability and 3 other fieldsHigh correlation
abnormal_short_term_variability is highly overall correlated with mean_value_of_short_term_variabilityHigh correlation
mean_value_of_short_term_variability is highly overall correlated with light_decelerations and 6 other fieldsHigh correlation
percentage_of_time_with_abnormal_long_term_variability is highly overall correlated with mean_value_of_short_term_variability and 2 other fieldsHigh correlation
histogram_width is highly overall correlated with light_decelerations and 6 other fieldsHigh correlation
histogram_min is highly overall correlated with light_decelerations and 4 other fieldsHigh correlation
histogram_max is highly overall correlated with histogram_width and 2 other fieldsHigh correlation
histogram_number_of_peaks is highly overall correlated with mean_value_of_short_term_variability and 4 other fieldsHigh correlation
histogram_mode is highly overall correlated with baseline value and 3 other fieldsHigh correlation
histogram_mean is highly overall correlated with baseline value and 2 other fieldsHigh correlation
histogram_median is highly overall correlated with baseline value and 2 other fieldsHigh correlation
histogram_variance is highly overall correlated with light_decelerations and 6 other fieldsHigh correlation
severe_decelerations is highly overall correlated with histogram_modeHigh correlation
severe_decelerations is highly imbalanced (96.8%)Imbalance
accelerations has 894 (42.1%) zerosZeros
fetal_movement has 1311 (61.7%) zerosZeros
uterine_contractions has 332 (15.6%) zerosZeros
light_decelerations has 1231 (57.9%) zerosZeros
prolongued_decelerations has 1948 (91.6%) zerosZeros
percentage_of_time_with_abnormal_long_term_variability has 1240 (58.3%) zerosZeros
mean_value_of_long_term_variability has 137 (6.4%) zerosZeros
histogram_number_of_peaks has 107 (5.0%) zerosZeros
histogram_number_of_zeroes has 1624 (76.4%) zerosZeros
histogram_variance has 187 (8.8%) zerosZeros

Reproduction

Analysis started2023-05-12 10:12:12.007008
Analysis finished2023-05-12 10:12:46.617179
Duration34.61 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

baseline value
Real number (ℝ)

Distinct48
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133.30386
Minimum106
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:46.751542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum106
5-th percentile119
Q1126
median133
Q3140
95-th percentile149
Maximum160
Range54
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.8408443
Coefficient of variation (CV)0.073822652
Kurtosis-0.29294291
Mean133.30386
Median Absolute Deviation (MAD)7
Skewness0.020312189
Sum283404
Variance96.842216
MonotonicityNot monotonic
2023-05-12T03:12:46.855987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
133 136
 
6.4%
130 111
 
5.2%
122 109
 
5.1%
138 103
 
4.8%
125 91
 
4.3%
128 85
 
4.0%
120 78
 
3.7%
142 77
 
3.6%
144 77
 
3.6%
132 76
 
3.6%
Other values (38) 1183
55.6%
ValueCountFrequency (%)
106 7
 
0.3%
110 21
 
1.0%
112 16
 
0.8%
114 11
 
0.5%
115 28
 
1.3%
116 5
 
0.2%
117 2
 
0.1%
118 9
 
0.4%
119 17
 
0.8%
120 78
3.7%
ValueCountFrequency (%)
160 1
 
< 0.1%
159 12
0.6%
158 10
 
0.5%
157 4
 
0.2%
156 4
 
0.2%
154 8
 
0.4%
152 17
0.8%
151 14
0.7%
150 26
1.2%
149 18
0.8%

accelerations
Real number (ℝ)

Distinct20
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003178269
Minimum0
Maximum0.019
Zeros894
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:46.947209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.002
Q30.006
95-th percentile0.011
Maximum0.019
Range0.019
Interquartile range (IQR)0.006

Descriptive statistics

Standard deviation0.003865591
Coefficient of variation (CV)1.2162567
Kurtosis0.76764826
Mean0.003178269
Median Absolute Deviation (MAD)0.002
Skewness1.2043921
Sum6.757
Variance1.4942793 × 10-5
MonotonicityNot monotonic
2023-05-12T03:12:47.024618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 894
42.1%
0.003 161
 
7.6%
0.002 160
 
7.5%
0.001 143
 
6.7%
0.004 117
 
5.5%
0.006 112
 
5.3%
0.005 110
 
5.2%
0.008 103
 
4.8%
0.007 91
 
4.3%
0.009 60
 
2.8%
Other values (10) 175
 
8.2%
ValueCountFrequency (%)
0 894
42.1%
0.001 143
 
6.7%
0.002 160
 
7.5%
0.003 161
 
7.6%
0.004 117
 
5.5%
0.005 110
 
5.2%
0.006 112
 
5.3%
0.007 91
 
4.3%
0.008 103
 
4.8%
0.009 60
 
2.8%
ValueCountFrequency (%)
0.019 1
 
< 0.1%
0.018 2
 
0.1%
0.017 4
 
0.2%
0.016 7
 
0.3%
0.015 9
 
0.4%
0.014 20
 
0.9%
0.013 22
1.0%
0.012 24
1.1%
0.011 36
1.7%
0.01 50
2.4%

fetal_movement
Real number (ℝ)

Distinct102
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.009480715
Minimum0
Maximum0.481
Zeros1311
Zeros (%)61.7%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:47.123233image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.003
95-th percentile0.028
Maximum0.481
Range0.481
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.046665844
Coefficient of variation (CV)4.9221862
Kurtosis64.260821
Mean0.009480715
Median Absolute Deviation (MAD)0
Skewness7.8114772
Sum20.156
Variance0.002177701
MonotonicityNot monotonic
2023-05-12T03:12:47.226067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1311
61.7%
0.001 164
 
7.7%
0.002 112
 
5.3%
0.003 88
 
4.1%
0.004 49
 
2.3%
0.005 36
 
1.7%
0.006 31
 
1.5%
0.007 28
 
1.3%
0.008 25
 
1.2%
0.01 25
 
1.2%
Other values (92) 257
 
12.1%
ValueCountFrequency (%)
0 1311
61.7%
0.001 164
 
7.7%
0.002 112
 
5.3%
0.003 88
 
4.1%
0.004 49
 
2.3%
0.005 36
 
1.7%
0.006 31
 
1.5%
0.007 28
 
1.3%
0.008 25
 
1.2%
0.009 25
 
1.2%
ValueCountFrequency (%)
0.481 1
< 0.1%
0.477 1
< 0.1%
0.47 1
< 0.1%
0.469 1
< 0.1%
0.455 1
< 0.1%
0.451 1
< 0.1%
0.446 1
< 0.1%
0.443 1
< 0.1%
0.441 1
< 0.1%
0.43 1
< 0.1%

uterine_contractions
Real number (ℝ)

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0043664158
Minimum0
Maximum0.015
Zeros332
Zeros (%)15.6%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:47.463073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.002
median0.004
Q30.007
95-th percentile0.009
Maximum0.015
Range0.015
Interquartile range (IQR)0.005

Descriptive statistics

Standard deviation0.0029460691
Coefficient of variation (CV)0.67471108
Kurtosis-0.63507123
Mean0.0043664158
Median Absolute Deviation (MAD)0.002
Skewness0.15931455
Sum9.283
Variance8.6793233 × 10-6
MonotonicityNot monotonic
2023-05-12T03:12:47.608433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 332
15.6%
0.005 290
13.6%
0.004 244
11.5%
0.006 231
10.9%
0.007 216
10.2%
0.003 212
10.0%
0.008 160
7.5%
0.002 160
7.5%
0.001 118
 
5.6%
0.009 82
 
3.9%
Other values (6) 81
 
3.8%
ValueCountFrequency (%)
0 332
15.6%
0.001 118
 
5.6%
0.002 160
7.5%
0.003 212
10.0%
0.004 244
11.5%
0.005 290
13.6%
0.006 231
10.9%
0.007 216
10.2%
0.008 160
7.5%
0.009 82
 
3.9%
ValueCountFrequency (%)
0.015 1
 
< 0.1%
0.014 2
 
0.1%
0.013 2
 
0.1%
0.012 11
 
0.5%
0.011 16
 
0.8%
0.01 49
 
2.3%
0.009 82
 
3.9%
0.008 160
7.5%
0.007 216
10.2%
0.006 231
10.9%

light_decelerations
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0018894638
Minimum0
Maximum0.015
Zeros1231
Zeros (%)57.9%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:47.815345image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.003
95-th percentile0.008
Maximum0.015
Range0.015
Interquartile range (IQR)0.003

Descriptive statistics

Standard deviation0.0029602086
Coefficient of variation (CV)1.5666924
Kurtosis2.5174609
Mean0.0018894638
Median Absolute Deviation (MAD)0
Skewness1.7184369
Sum4.017
Variance8.7628348 × 10-6
MonotonicityNot monotonic
2023-05-12T03:12:47.974276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 1231
57.9%
0.001 163
 
7.7%
0.003 118
 
5.6%
0.002 115
 
5.4%
0.004 114
 
5.4%
0.005 107
 
5.0%
0.006 74
 
3.5%
0.008 55
 
2.6%
0.007 54
 
2.5%
0.009 37
 
1.7%
Other values (6) 58
 
2.7%
ValueCountFrequency (%)
0 1231
57.9%
0.001 163
 
7.7%
0.002 115
 
5.4%
0.003 118
 
5.6%
0.004 114
 
5.4%
0.005 107
 
5.0%
0.006 74
 
3.5%
0.007 54
 
2.5%
0.008 55
 
2.6%
0.009 37
 
1.7%
ValueCountFrequency (%)
0.015 3
 
0.1%
0.014 7
 
0.3%
0.013 8
 
0.4%
0.012 12
 
0.6%
0.011 13
 
0.6%
0.01 15
 
0.7%
0.009 37
1.7%
0.008 55
2.6%
0.007 54
2.5%
0.006 74
3.5%

severe_decelerations
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size124.7 KiB
0.0
2119 
0.001
 
7

Length

Max length5
Median length3
Mean length3.0065851
Min length3

Characters and Unicode

Total characters6392
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2119
99.7%
0.001 7
 
0.3%

Length

2023-05-12T03:12:48.149549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T03:12:48.346323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2119
99.7%
0.001 7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 4259
66.6%
. 2126
33.3%
1 7
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4266
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4259
99.8%
1 7
 
0.2%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4259
66.6%
. 2126
33.3%
1 7
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4259
66.6%
. 2126
33.3%
1 7
 
0.1%

prolongued_decelerations
Real number (ℝ)

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00015851364
Minimum0
Maximum0.005
Zeros1948
Zeros (%)91.6%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:48.464440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.002
Maximum0.005
Range0.005
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.00058994752
Coefficient of variation (CV)3.7217461
Kurtosis20.515918
Mean0.00015851364
Median Absolute Deviation (MAD)0
Skewness4.3239651
Sum0.337
Variance3.4803807 × 10-7
MonotonicityNot monotonic
2023-05-12T03:12:48.594954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1948
91.6%
0.002 72
 
3.4%
0.001 70
 
3.3%
0.003 24
 
1.1%
0.004 9
 
0.4%
0.005 3
 
0.1%
ValueCountFrequency (%)
0 1948
91.6%
0.001 70
 
3.3%
0.002 72
 
3.4%
0.003 24
 
1.1%
0.004 9
 
0.4%
0.005 3
 
0.1%
ValueCountFrequency (%)
0.005 3
 
0.1%
0.004 9
 
0.4%
0.003 24
 
1.1%
0.002 72
 
3.4%
0.001 70
 
3.3%
0 1948
91.6%
Distinct75
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.990122
Minimum12
Maximum87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:48.736892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile21
Q132
median49
Q361
95-th percentile75
Maximum87
Range75
Interquartile range (IQR)29

Descriptive statistics

Standard deviation17.192814
Coefficient of variation (CV)0.36588144
Kurtosis-1.0510296
Mean46.990122
Median Absolute Deviation (MAD)14
Skewness-0.011828576
Sum99901
Variance295.59284
MonotonicityNot monotonic
2023-05-12T03:12:48.945357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 62
 
2.9%
58 61
 
2.9%
65 60
 
2.8%
63 58
 
2.7%
64 58
 
2.7%
61 57
 
2.7%
51 54
 
2.5%
62 51
 
2.4%
22 48
 
2.3%
25 46
 
2.2%
Other values (65) 1571
73.9%
ValueCountFrequency (%)
12 2
 
0.1%
13 7
 
0.3%
14 4
 
0.2%
15 4
 
0.2%
16 12
 
0.6%
17 13
 
0.6%
18 10
 
0.5%
19 19
0.9%
20 27
1.3%
21 33
1.6%
ValueCountFrequency (%)
87 1
 
< 0.1%
86 4
 
0.2%
84 6
 
0.3%
83 4
 
0.2%
82 2
 
0.1%
81 7
 
0.3%
80 7
 
0.3%
79 15
0.7%
78 19
0.9%
77 16
0.8%
Distinct57
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3327846
Minimum0.2
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:49.146562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.7
median1.2
Q31.7
95-th percentile3
Maximum7
Range6.8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.88324133
Coefficient of variation (CV)0.66270375
Kurtosis4.7007563
Mean1.3327846
Median Absolute Deviation (MAD)0.5
Skewness1.6573392
Sum2833.5
Variance0.78011525
MonotonicityNot monotonic
2023-05-12T03:12:49.302885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 125
 
5.9%
1.3 121
 
5.7%
0.5 121
 
5.7%
0.4 120
 
5.6%
0.7 117
 
5.5%
0.9 114
 
5.4%
0.6 113
 
5.3%
1.2 107
 
5.0%
1.5 100
 
4.7%
1 99
 
4.7%
Other values (47) 989
46.5%
ValueCountFrequency (%)
0.2 47
 
2.2%
0.3 84
4.0%
0.4 120
5.6%
0.5 121
5.7%
0.6 113
5.3%
0.7 117
5.5%
0.8 125
5.9%
0.9 114
5.4%
1 99
4.7%
1.1 97
4.6%
ValueCountFrequency (%)
7 1
< 0.1%
6.9 1
< 0.1%
6.3 2
0.1%
6 1
< 0.1%
5.9 1
< 0.1%
5.7 1
< 0.1%
5.4 2
0.1%
5.3 1
< 0.1%
5.2 1
< 0.1%
5 2
0.1%

percentage_of_time_with_abnormal_long_term_variability
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct87
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8466604
Minimum0
Maximum91
Zeros1240
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:49.455892image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q311
95-th percentile56
Maximum91
Range91
Interquartile range (IQR)11

Descriptive statistics

Standard deviation18.39688
Coefficient of variation (CV)1.868337
Kurtosis4.2529979
Mean9.8466604
Median Absolute Deviation (MAD)0
Skewness2.1950753
Sum20934
Variance338.44518
MonotonicityNot monotonic
2023-05-12T03:12:49.612485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1240
58.3%
1 52
 
2.4%
2 45
 
2.1%
5 43
 
2.0%
4 40
 
1.9%
3 36
 
1.7%
8 34
 
1.6%
6 31
 
1.5%
12 29
 
1.4%
10 23
 
1.1%
Other values (77) 553
26.0%
ValueCountFrequency (%)
0 1240
58.3%
1 52
 
2.4%
2 45
 
2.1%
3 36
 
1.7%
4 40
 
1.9%
5 43
 
2.0%
6 31
 
1.5%
7 23
 
1.1%
8 34
 
1.6%
9 22
 
1.0%
ValueCountFrequency (%)
91 4
0.2%
90 2
 
0.1%
88 1
 
< 0.1%
86 1
 
< 0.1%
85 1
 
< 0.1%
84 6
0.3%
82 1
 
< 0.1%
81 2
 
0.1%
79 1
 
< 0.1%
78 3
0.1%
Distinct249
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1876294
Minimum0
Maximum50.7
Zeros137
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:49.817099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7.4
Q310.8
95-th percentile18.475
Maximum50.7
Range50.7
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation5.6282466
Coefficient of variation (CV)0.68740857
Kurtosis4.1312538
Mean8.1876294
Median Absolute Deviation (MAD)3.1
Skewness1.3319979
Sum17406.9
Variance31.67716
MonotonicityNot monotonic
2023-05-12T03:12:50.031390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 137
 
6.4%
7.1 29
 
1.4%
6.7 29
 
1.4%
6.5 25
 
1.2%
5.2 25
 
1.2%
9.5 24
 
1.1%
6.8 23
 
1.1%
5.6 23
 
1.1%
7.2 23
 
1.1%
8.5 23
 
1.1%
Other values (239) 1765
83.0%
ValueCountFrequency (%)
0 137
6.4%
0.1 4
 
0.2%
0.2 4
 
0.2%
0.3 9
 
0.4%
0.4 6
 
0.3%
0.5 11
 
0.5%
0.6 3
 
0.1%
0.7 4
 
0.2%
0.8 1
 
< 0.1%
0.9 5
 
0.2%
ValueCountFrequency (%)
50.7 1
< 0.1%
41.8 1
< 0.1%
40.8 1
< 0.1%
36.9 1
< 0.1%
35.7 1
< 0.1%
34.7 1
< 0.1%
33.5 1
< 0.1%
29.6 1
< 0.1%
29.5 1
< 0.1%
29.3 1
< 0.1%

histogram_width
Real number (ℝ)

Distinct154
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.445908
Minimum3
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:50.228967image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile16
Q137
median67.5
Q3100
95-th percentile138
Maximum180
Range177
Interquartile range (IQR)63

Descriptive statistics

Standard deviation38.955693
Coefficient of variation (CV)0.55298731
Kurtosis-0.90228678
Mean70.445908
Median Absolute Deviation (MAD)31.5
Skewness0.31423475
Sum149768
Variance1517.546
MonotonicityNot monotonic
2023-05-12T03:12:50.427280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 42
 
2.0%
102 35
 
1.6%
27 30
 
1.4%
31 29
 
1.4%
90 28
 
1.3%
98 28
 
1.3%
96 27
 
1.3%
83 27
 
1.3%
22 27
 
1.3%
42 26
 
1.2%
Other values (144) 1827
85.9%
ValueCountFrequency (%)
3 2
 
0.1%
5 2
 
0.1%
6 1
 
< 0.1%
7 3
 
0.1%
8 10
0.5%
9 6
 
0.3%
10 9
0.4%
11 10
0.5%
12 20
0.9%
13 13
0.6%
ValueCountFrequency (%)
180 1
 
< 0.1%
176 6
0.3%
163 2
 
0.1%
162 1
 
< 0.1%
161 5
0.2%
158 2
 
0.1%
153 3
 
0.1%
150 10
0.5%
149 11
0.5%
148 8
0.4%

histogram_min
Real number (ℝ)

Distinct109
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.579492
Minimum50
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:50.614831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile51
Q167
median93
Q3120
95-th percentile139
Maximum159
Range109
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.560212
Coefficient of variation (CV)0.31588344
Kurtosis-1.2904222
Mean93.579492
Median Absolute Deviation (MAD)27
Skewness0.11578402
Sum198950
Variance873.80615
MonotonicityNot monotonic
2023-05-12T03:12:50.849688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 77
 
3.6%
52 50
 
2.4%
71 49
 
2.3%
120 48
 
2.3%
60 45
 
2.1%
68 43
 
2.0%
67 41
 
1.9%
103 40
 
1.9%
51 36
 
1.7%
62 35
 
1.6%
Other values (99) 1662
78.2%
ValueCountFrequency (%)
50 77
3.6%
51 36
1.7%
52 50
2.4%
53 32
1.5%
54 27
 
1.3%
55 20
 
0.9%
56 19
 
0.9%
57 22
 
1.0%
58 22
 
1.0%
59 17
 
0.8%
ValueCountFrequency (%)
159 1
 
< 0.1%
158 1
 
< 0.1%
156 1
 
< 0.1%
155 2
 
0.1%
154 3
 
0.1%
153 8
0.4%
152 4
0.2%
151 4
0.2%
150 3
 
0.1%
149 2
 
0.1%

histogram_max
Real number (ℝ)

Distinct86
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.0254
Minimum122
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:51.037398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum122
5-th percentile138
Q1152
median162
Q3174
95-th percentile198
Maximum238
Range116
Interquartile range (IQR)22

Descriptive statistics

Standard deviation17.944183
Coefficient of variation (CV)0.10939881
Kurtosis0.63276948
Mean164.0254
Median Absolute Deviation (MAD)11
Skewness0.57786245
Sum348718
Variance321.99371
MonotonicityNot monotonic
2023-05-12T03:12:51.272524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157 71
 
3.3%
171 66
 
3.1%
158 62
 
2.9%
156 60
 
2.8%
159 58
 
2.7%
152 54
 
2.5%
154 52
 
2.4%
178 52
 
2.4%
172 48
 
2.3%
153 48
 
2.3%
Other values (76) 1555
73.1%
ValueCountFrequency (%)
122 2
 
0.1%
123 2
 
0.1%
125 3
 
0.1%
126 5
0.2%
127 2
 
0.1%
128 4
 
0.2%
129 10
0.5%
130 8
0.4%
131 7
0.3%
132 4
 
0.2%
ValueCountFrequency (%)
238 6
 
0.3%
230 3
 
0.1%
228 5
 
0.2%
213 1
 
< 0.1%
211 5
 
0.2%
210 4
 
0.2%
205 1
 
< 0.1%
204 3
 
0.1%
200 31
1.5%
199 20
0.9%

histogram_number_of_peaks
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0682032
Minimum0
Maximum18
Zeros107
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:51.492867image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q12
median3
Q36
95-th percentile10
Maximum18
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9493856
Coefficient of variation (CV)0.72498483
Kurtosis0.50421053
Mean4.0682032
Median Absolute Deviation (MAD)2
Skewness0.89288591
Sum8649
Variance8.6988755
MonotonicityNot monotonic
2023-05-12T03:12:51.601293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 357
16.8%
2 331
15.6%
3 269
12.7%
4 258
12.1%
5 210
9.9%
6 158
7.4%
7 145
6.8%
0 107
 
5.0%
8 106
 
5.0%
9 67
 
3.2%
Other values (8) 118
 
5.6%
ValueCountFrequency (%)
0 107
 
5.0%
1 357
16.8%
2 331
15.6%
3 269
12.7%
4 258
12.1%
5 210
9.9%
6 158
7.4%
7 145
6.8%
8 106
 
5.0%
9 67
 
3.2%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 2
 
0.1%
15 1
 
< 0.1%
14 5
 
0.2%
13 10
 
0.5%
12 22
 
1.0%
11 28
 
1.3%
10 49
2.3%
9 67
3.2%
8 106
5.0%
Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32361242
Minimum0
Maximum10
Zeros1624
Zeros (%)76.4%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:51.741826image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70605937
Coefficient of variation (CV)2.1818056
Kurtosis30.365084
Mean0.32361242
Median Absolute Deviation (MAD)0
Skewness3.9202874
Sum688
Variance0.49851984
MonotonicityNot monotonic
2023-05-12T03:12:51.891299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 1624
76.4%
1 366
 
17.2%
2 108
 
5.1%
3 21
 
1.0%
4 2
 
0.1%
5 2
 
0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 1624
76.4%
1 366
 
17.2%
2 108
 
5.1%
3 21
 
1.0%
4 2
 
0.1%
5 2
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
10 1
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
5 2
 
0.1%
4 2
 
0.1%
3 21
 
1.0%
2 108
 
5.1%
1 366
 
17.2%
0 1624
76.4%

histogram_mode
Real number (ℝ)

Distinct88
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137.45202
Minimum60
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:52.091148image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile111.25
Q1129
median139
Q3148
95-th percentile160
Maximum187
Range127
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.381289
Coefficient of variation (CV)0.11917823
Kurtosis3.0095305
Mean137.45202
Median Absolute Deviation (MAD)10
Skewness-0.99517784
Sum292223
Variance268.34664
MonotonicityNot monotonic
2023-05-12T03:12:52.300419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
133 140
 
6.6%
136 89
 
4.2%
150 89
 
4.2%
142 87
 
4.1%
148 79
 
3.7%
144 78
 
3.7%
129 76
 
3.6%
143 71
 
3.3%
125 67
 
3.2%
126 66
 
3.1%
Other values (78) 1284
60.4%
ValueCountFrequency (%)
60 6
0.3%
67 5
0.2%
69 1
 
< 0.1%
71 1
 
< 0.1%
75 6
0.3%
76 1
 
< 0.1%
77 1
 
< 0.1%
86 11
0.5%
88 6
0.3%
89 3
 
0.1%
ValueCountFrequency (%)
187 1
 
< 0.1%
186 6
0.3%
180 4
 
0.2%
179 1
 
< 0.1%
176 6
0.3%
170 4
 
0.2%
169 3
 
0.1%
167 8
0.4%
165 10
0.5%
164 1
 
< 0.1%

histogram_mean
Real number (ℝ)

Distinct103
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean134.61054
Minimum73
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:52.540321image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile108
Q1125
median136
Q3145
95-th percentile157
Maximum182
Range109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.593596
Coefficient of variation (CV)0.11584232
Kurtosis0.93342749
Mean134.61054
Median Absolute Deviation (MAD)10
Skewness-0.65101924
Sum286182
Variance243.16025
MonotonicityNot monotonic
2023-05-12T03:12:52.712824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
143 65
 
3.1%
144 64
 
3.0%
135 63
 
3.0%
141 61
 
2.9%
140 60
 
2.8%
132 59
 
2.8%
133 58
 
2.7%
145 58
 
2.7%
136 57
 
2.7%
147 57
 
2.7%
Other values (93) 1524
71.7%
ValueCountFrequency (%)
73 1
 
< 0.1%
75 1
 
< 0.1%
76 1
 
< 0.1%
78 1
 
< 0.1%
79 1
 
< 0.1%
80 2
0.1%
81 1
 
< 0.1%
82 2
0.1%
83 4
0.2%
84 3
0.1%
ValueCountFrequency (%)
182 1
< 0.1%
180 1
< 0.1%
178 1
< 0.1%
175 1
< 0.1%
173 2
0.1%
172 1
< 0.1%
171 2
0.1%
170 1
< 0.1%
169 1
< 0.1%
168 1
< 0.1%

histogram_median
Real number (ℝ)

Distinct95
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean138.09031
Minimum77
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:52.900307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum77
5-th percentile113
Q1129
median139
Q3148
95-th percentile159
Maximum186
Range109
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.466589
Coefficient of variation (CV)0.1047618
Kurtosis0.66725933
Mean138.09031
Median Absolute Deviation (MAD)10
Skewness-0.4784142
Sum293580
Variance209.28219
MonotonicityNot monotonic
2023-05-12T03:12:53.074369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
146 69
 
3.2%
137 68
 
3.2%
142 68
 
3.2%
145 67
 
3.2%
147 65
 
3.1%
151 63
 
3.0%
141 63
 
3.0%
134 62
 
2.9%
149 60
 
2.8%
143 56
 
2.6%
Other values (85) 1485
69.8%
ValueCountFrequency (%)
77 1
< 0.1%
78 1
< 0.1%
79 2
0.1%
82 1
< 0.1%
86 1
< 0.1%
87 1
< 0.1%
90 1
< 0.1%
91 1
< 0.1%
92 2
0.1%
93 1
< 0.1%
ValueCountFrequency (%)
186 1
< 0.1%
183 1
< 0.1%
180 1
< 0.1%
178 1
< 0.1%
177 1
< 0.1%
176 2
0.1%
174 2
0.1%
172 1
< 0.1%
171 1
< 0.1%
170 2
0.1%

histogram_variance
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct133
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.80809
Minimum0
Maximum269
Zeros187
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2023-05-12T03:12:53.246630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median7
Q324
95-th percentile76
Maximum269
Range269
Interquartile range (IQR)22

Descriptive statistics

Standard deviation28.977636
Coefficient of variation (CV)1.5407006
Kurtosis15.131589
Mean18.80809
Median Absolute Deviation (MAD)6
Skewness3.2199738
Sum39986
Variance839.70339
MonotonicityNot monotonic
2023-05-12T03:12:53.497619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 248
 
11.7%
0 187
 
8.8%
2 166
 
7.8%
3 161
 
7.6%
4 108
 
5.1%
5 85
 
4.0%
8 74
 
3.5%
6 65
 
3.1%
7 53
 
2.5%
9 49
 
2.3%
Other values (123) 930
43.7%
ValueCountFrequency (%)
0 187
8.8%
1 248
11.7%
2 166
7.8%
3 161
7.6%
4 108
5.1%
5 85
 
4.0%
6 65
 
3.1%
7 53
 
2.5%
8 74
 
3.5%
9 49
 
2.3%
ValueCountFrequency (%)
269 1
< 0.1%
254 1
< 0.1%
250 1
< 0.1%
243 1
< 0.1%
241 1
< 0.1%
215 1
< 0.1%
195 1
< 0.1%
190 1
< 0.1%
182 1
< 0.1%
177 1
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size124.9 KiB
0.0
1115 
1.0
846 
-1.0
165 

Length

Max length4
Median length3
Mean length3.0776105
Min length3

Characters and Unicode

Total characters6543
Distinct characters4
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 1115
52.4%
1.0 846
39.8%
-1.0 165
 
7.8%

Length

2023-05-12T03:12:53.716197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T03:12:53.904862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1115
52.4%
1.0 1011
47.6%

Most occurring characters

ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
65.0%
Other Punctuation 2126
32.5%
Dash Punctuation 165
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3241
76.2%
1 1011
 
23.8%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 165
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6543
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6543
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3241
49.5%
. 2126
32.5%
1 1011
 
15.5%
- 165
 
2.5%

fetal_health
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size124.7 KiB
1.0
1655 
2.0
295 
3.0
176 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6378
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 1655
77.8%
2.0 295
 
13.9%
3.0 176
 
8.3%

Length

2023-05-12T03:12:54.045558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-12T03:12:54.202927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.0 1655
77.8%
2.0 295
 
13.9%
3.0 176
 
8.3%

Most occurring characters

ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4252
66.7%
Other Punctuation 2126
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2126
50.0%
1 1655
38.9%
2 295
 
6.9%
3 176
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 2126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6378
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2126
33.3%
0 2126
33.3%
1 1655
25.9%
2 295
 
4.6%
3 176
 
2.8%

Interactions

2023-05-12T03:12:44.245602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:13.071282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:14.852615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:16.578988image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:18.345191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:20.092806image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:21.769674image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:23.591002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:25.357305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:27.070186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:28.758486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:30.599722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:32.237380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:33.888477image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:35.526252image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:37.494837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:39.141828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:40.860258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-12T03:12:20.182102image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:21.849482image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:23.676411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:25.447512image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:27.153905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:28.837701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:30.682107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:32.317693image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:33.972515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:35.611229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:37.572659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:39.231270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:40.940273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:42.644987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-12T03:12:20.273515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:21.941471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:23.769068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:25.544118image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:27.243104image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:28.931832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:30.770962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:32.406523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:34.060917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-12T03:12:41.034430image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-12T03:12:44.511880image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-12T03:12:22.020520image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:23.858329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:25.632733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:27.370990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:29.011181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:30.853860image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:32.527377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:34.144168image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-12T03:12:37.739529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
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2023-05-12T03:12:45.120787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:13.941094image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:15.772794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:17.452128image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:19.279732image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:20.986221image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:22.615124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:24.526456image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:26.264078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:27.982996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:29.838739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:31.461136image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:33.142706image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:34.751224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:36.683604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:38.348598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:40.061460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:41.794845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:43.467572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:45.207415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:14.030794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:15.864274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:17.537226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:19.377375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:21.076162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:22.699759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:24.616541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:26.354001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:28.074778image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:29.924637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:31.547661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:33.233844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:34.837404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:36.776084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:38.436349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:40.158795image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:41.880336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:43.558837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:45.288944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:14.111101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:15.948366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:17.621696image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:19.464030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:21.162084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:22.821130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:24.706330image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:26.444609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:28.158621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:30.009003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:31.630342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:33.311471image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:34.920763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:36.860624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:38.514481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:40.240677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:41.961103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:43.640687image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:45.375487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:14.197230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:16.038737image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:17.706529image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:19.551337image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:21.248740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:22.905385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:24.801823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:26.539621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:28.247383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:30.099342image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:31.716202image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:33.392861image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:35.017718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:36.970472image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:38.602758image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:40.327103image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:42.048093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:43.733377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:45.464589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:14.421035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:16.135523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:17.797404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:19.648306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:21.341058image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:22.992338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:24.902174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:26.639273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:28.336972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:30.186350image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:31.807422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:33.480977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:35.101995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:37.061425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:38.690912image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:40.422400image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:42.135827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:43.824034image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:45.543557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:14.503928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:16.223055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:17.876929image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:19.737935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:21.423577image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:23.237716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:24.989930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:26.726818image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:28.419937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:30.269991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:31.892306image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:33.560340image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:35.181408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:37.145149image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:38.766623image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:40.509234image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:42.227042image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:43.908302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:45.942197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:14.595562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:16.311816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:17.963295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:19.828185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:21.513033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:23.326771image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:25.083514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:26.812979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:28.505357image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:30.354816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:31.986930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:33.642688image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:35.264657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:37.232457image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:38.866475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:40.590762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:42.312524image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:43.987753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:46.021418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:14.684051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:16.399338image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:18.044641image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:19.911752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:21.597671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:23.414516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:25.174289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:26.900228image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:28.591455image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:30.436159image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:32.071733image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:33.720382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:35.351606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:37.327050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:38.947429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:40.674356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:42.403339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:44.076751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:46.104404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:14.769593image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:16.487278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:18.128796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:19.997735image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:21.681087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:23.508276image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:25.266277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:26.985957image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:28.673068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:30.518287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:32.152583image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:33.800398image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:35.445087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:37.408792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:39.030282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:40.765836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:42.482184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-05-12T03:12:44.158990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-05-12T03:12:54.375160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
baseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancesevere_decelerationshistogram_tendencyfetal_health
baseline value1.000-0.113-0.022-0.134-0.174-0.1410.317-0.3670.340-0.055-0.1550.3580.326-0.121-0.0650.8180.7880.841-0.2390.0860.2750.295
accelerations-0.1131.0000.0520.120-0.012-0.116-0.3440.327-0.491-0.1020.352-0.1670.4700.2380.0010.1870.2100.2090.3910.0000.0990.314
fetal_movement-0.0220.0521.000-0.3100.0350.1230.2600.071-0.075-0.1140.188-0.1800.1160.193-0.093-0.035-0.080-0.0410.1150.0000.0970.165
uterine_contractions-0.1340.120-0.3101.0000.3030.126-0.2180.327-0.283-0.0670.139-0.1040.1130.1180.061-0.089-0.171-0.1350.2770.0000.0870.238
light_decelerations-0.174-0.0120.0350.3031.0000.327-0.1530.617-0.385-0.2500.584-0.6020.2330.4820.276-0.252-0.470-0.3300.7130.2360.1000.195
prolongued_decelerations-0.141-0.1160.1230.1260.3271.0000.0300.301-0.219-0.2380.287-0.3100.1050.2410.080-0.311-0.405-0.3580.3800.0660.2040.413
abnormal_short_term_variability0.317-0.3440.260-0.218-0.1530.0301.000-0.5210.425-0.338-0.2790.269-0.126-0.166-0.1740.1260.1370.164-0.3450.1140.1570.453
mean_value_of_short_term_variability-0.3670.3270.0710.3270.6170.301-0.5211.000-0.685-0.0200.714-0.6950.3720.5520.296-0.313-0.460-0.3590.7790.0480.0560.333
percentage_of_time_with_abnormal_long_term_variability0.340-0.491-0.075-0.283-0.385-0.2190.425-0.6851.000-0.043-0.5260.465-0.321-0.359-0.1650.2220.2920.245-0.6500.0000.0270.479
mean_value_of_long_term_variability-0.055-0.102-0.114-0.067-0.250-0.238-0.338-0.020-0.0431.0000.048-0.081-0.046-0.0020.0720.0020.0770.011-0.0930.0000.1010.220
histogram_width-0.1550.3520.1880.1390.5840.287-0.2790.714-0.5260.0481.000-0.9050.6490.7750.332-0.103-0.234-0.1350.8300.0970.2460.292
histogram_min0.358-0.167-0.180-0.104-0.602-0.3100.269-0.6950.465-0.081-0.9051.000-0.292-0.700-0.3270.3460.4710.387-0.7510.0830.2140.313
histogram_max0.3260.4700.1160.1130.2330.105-0.1260.372-0.321-0.0460.649-0.2921.0000.5020.1680.4120.3420.4130.5270.0500.1700.143
histogram_number_of_peaks-0.1210.2380.1930.1180.4820.241-0.1660.552-0.359-0.0020.775-0.7000.5021.0000.293-0.072-0.191-0.1060.6470.0000.1420.124
histogram_number_of_zeroes-0.0650.001-0.0930.0610.2760.080-0.1740.296-0.1650.0720.332-0.3270.1680.2931.000-0.073-0.116-0.0740.3020.0000.0400.047
histogram_mode0.8180.187-0.035-0.089-0.252-0.3110.126-0.3130.2220.002-0.1030.3460.412-0.072-0.0731.0000.9080.961-0.1520.5290.3700.433
histogram_mean0.7880.210-0.080-0.171-0.470-0.4050.137-0.4600.2920.077-0.2340.4710.342-0.191-0.1160.9081.0000.958-0.3250.4040.3060.496
histogram_median0.8410.209-0.041-0.135-0.330-0.3580.164-0.3590.2450.011-0.1350.3870.413-0.106-0.0740.9610.9581.000-0.2160.4680.3550.434
histogram_variance-0.2390.3910.1150.2770.7130.380-0.3450.779-0.650-0.0930.830-0.7510.5270.6470.302-0.152-0.325-0.2161.0000.2300.1450.267
severe_decelerations0.0860.0000.0000.0000.2360.0660.1140.0480.0000.0000.0970.0830.0500.0000.0000.5290.4040.4680.2301.0000.1350.159
histogram_tendency0.2750.0990.0970.0870.1000.2040.1570.0560.0270.1010.2460.2140.1700.1420.0400.3700.3060.3550.1450.1351.0000.169
fetal_health0.2950.3140.1650.2380.1950.4130.4530.3330.4790.2200.2920.3130.1430.1240.0470.4330.4960.4340.2670.1590.1691.000

Missing values

2023-05-12T03:12:46.235942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-12T03:12:46.491966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

baseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_health
0120.00.0000.00.0000.0000.00.00073.00.543.02.464.062.0126.02.00.0120.0137.0121.073.01.02.0
1132.00.0060.00.0060.0030.00.00017.02.10.010.4130.068.0198.06.01.0141.0136.0140.012.00.01.0
2133.00.0030.00.0080.0030.00.00016.02.10.013.4130.068.0198.05.01.0141.0135.0138.013.00.01.0
3134.00.0030.00.0080.0030.00.00016.02.40.023.0117.053.0170.011.00.0137.0134.0137.013.01.01.0
4132.00.0070.00.0080.0000.00.00016.02.40.019.9117.053.0170.09.00.0137.0136.0138.011.01.01.0
5134.00.0010.00.0100.0090.00.00226.05.90.00.0150.050.0200.05.03.076.0107.0107.0170.00.03.0
6134.00.0010.00.0130.0080.00.00329.06.30.00.0150.050.0200.06.03.071.0107.0106.0215.00.03.0
7122.00.0000.00.0000.0000.00.00083.00.56.015.668.062.0130.00.00.0122.0122.0123.03.01.03.0
8122.00.0000.00.0020.0000.00.00084.00.55.013.668.062.0130.00.00.0122.0122.0123.03.01.03.0
9122.00.0000.00.0030.0000.00.00086.00.36.010.668.062.0130.01.00.0122.0122.0123.01.01.03.0
baseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_health
2116140.00.0040.0000.0040.0000.00.080.00.236.02.218.0140.0158.01.00.0147.0148.0149.01.00.01.0
2117140.00.0000.0000.0080.0000.00.079.00.320.08.526.0124.0150.01.00.0144.0143.0145.01.01.01.0
2118140.00.0000.0000.0060.0010.00.079.00.526.07.021.0129.0150.01.00.0145.0142.0145.02.01.01.0
2119140.00.0000.0000.0070.0010.00.079.00.627.06.426.0124.0150.01.00.0144.0141.0145.01.01.01.0
2120140.00.0000.0000.0050.0010.00.077.00.717.06.031.0124.0155.02.00.0145.0143.0145.02.00.01.0
2121140.00.0000.0000.0070.0000.00.079.00.225.07.240.0137.0177.04.00.0153.0150.0152.02.00.02.0
2122140.00.0010.0000.0070.0000.00.078.00.422.07.166.0103.0169.06.00.0152.0148.0151.03.01.02.0
2123140.00.0010.0000.0070.0000.00.079.00.420.06.167.0103.0170.05.00.0153.0148.0152.04.01.02.0
2124140.00.0010.0000.0060.0000.00.078.00.427.07.066.0103.0169.06.00.0152.0147.0151.04.01.02.0
2125142.00.0020.0020.0080.0000.00.074.00.436.05.042.0117.0159.02.01.0145.0143.0145.01.00.01.0

Duplicate rows

Most frequently occurring

baseline valueaccelerationsfetal_movementuterine_contractionslight_decelerationssevere_decelerationsprolongued_decelerationsabnormal_short_term_variabilitymean_value_of_short_term_variabilitypercentage_of_time_with_abnormal_long_term_variabilitymean_value_of_long_term_variabilityhistogram_widthhistogram_minhistogram_maxhistogram_number_of_peakshistogram_number_of_zeroeshistogram_modehistogram_meanhistogram_medianhistogram_variancehistogram_tendencyfetal_health# duplicates
0122.00.0000.0000.0000.00.00.019.01.90.015.139.0103.0142.01.00.0120.0120.0122.03.00.01.04
1123.00.0000.0000.0000.00.00.049.00.87.013.874.063.0137.02.00.0129.0127.0129.02.01.01.02
2123.00.0030.0030.0000.00.00.052.00.82.015.490.050.0140.07.00.0129.0128.0130.04.01.01.02
3123.00.0030.0040.0000.00.00.050.00.94.014.882.058.0140.07.00.0129.0128.0130.05.01.01.02
4135.00.0000.0000.0000.00.00.062.00.571.06.997.071.0168.03.00.0143.0142.0144.01.01.03.02
5138.00.0020.0000.0040.00.00.041.00.88.010.351.0105.0156.04.00.0142.0142.0143.02.01.01.02
6140.00.0070.0000.0040.00.00.034.01.20.010.360.0119.0179.02.00.0156.0153.0155.05.00.01.02
7144.00.0000.0190.0000.00.00.076.00.461.010.681.071.0152.03.00.0145.0144.0146.02.01.02.02
8145.00.0000.0200.0000.00.00.077.00.245.05.821.0129.0150.01.00.0146.0145.0147.00.01.02.02
9146.00.0000.0000.0030.00.00.065.00.439.07.019.0137.0156.01.00.0150.0149.0151.01.01.02.02